af-skill-write-agent-benchmarks

You can benchmark AI agents by running them through standardized scenarios within isolated sandboxes, applying evidence-based verification using a trace and judge to produce deterministic, reproducible performance outcomes.

Evidence-based testing for AI agents is a protocol that verifies performance by imposing standardized outcome reporting, deterministic runs, and trace-based judging within controlled sandbox environments.

To set up a sandbox for AI agent benchmarking, define your benchmarking goal, design an isolated environment, create a testing scenario, and execute it through a runner to obtain a structured verdict.

Yes, the benchmarking protocol applies to testing agents across coding, data analysis, and conversational tasks within production-like scenarios to verify their performance.

The best way to verify AI agent performance is through end-to-end benchmarking with a standard evidence-based protocol, utilizing deterministic evaluation in isolated sandboxes with a judge and trace.